Boosting Meta-Learning for Few-Shot Text Classification via Label-guided Distance Scaling
Few-shot text classification aims to recognize unseen classes with limited labeled text samples. Existing approaches focus on boosting meta-learners by developing complex algorithms in the training stage. However, the labeled samples are randomly selected during the testing stage, so they may not provide effective supervision signals, leading to misclassification. To address this issue, we propose a \textbf{L}abel-guided \textbf{D}istance \textbf{S}caling (LDS) strategy. The core of our method is exploiting label semantics as supervision signals in both the training and testing stages. Specifically, in the training stage, we design a label-guided loss to inject label semantic information,...
| Authors | Yunlong Gao, Xinyue Liu, Yingbo Wang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 28 February 2026 |
| Last updated at source | 28 February 2026 |
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